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feat: Arize Phoenix via OpenInference (#3136)
## Summary This adds Arize instrumentation via the OpenInference auto instrumentor. It requires the [related PR](Arize-ai/openinference#1603) to be merged first. ## Type of change - [ ] Bug fix - [x] New feature - [ ] Breaking change - [ ] Improvement - [ ] Model update - [ ] Other: --- ## Checklist - [ ] Code complies with style guidelines - [ ] Ran format/validation scripts (`./scripts/format.sh` and `./scripts/validate.sh`) - [ ] Self-review completed - [ ] Documentation updated (comments, docstrings) - [ ] Examples and guides: Relevant cookbook examples have been included or updated (if applicable) - [ ] Tested in clean environment - [ ] Tests added/updated (if applicable) --- ## Additional Notes Add any important context (deployment instructions, screenshots, security considerations, etc.) --------- Co-authored-by: manuhortet <manuhortet@gmail.com>
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‎cookbook/observability/arize_phoenix_via_openinference.py‎

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"""
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This example shows how to instrument your agno agent with OpenInference and send traces to Arize Phoenix.
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1. Install dependencies: pip install arize-phoenix openai openinference-instrumentation-agno opentelemetry-sdk opentelemetry-exporter-otlp
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2. Setup your Arize Phoenix account and get your API key: https://phoenix.arize.com/.
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3. Set your Arize Phoenix API key as an environment variable:
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- export ARIZE_PHOENIX_API_KEY=<your-key>
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"""
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import asyncio
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import os
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from agno.agent import Agent
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from agno.models.openai import OpenAIChat
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from agno.tools.yfinance import YFinanceTools
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from phoenix.otel import register
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os.environ["PHOENIX_CLIENT_HEADERS"] = f"api_key={os.getenv('ARIZE_PHOENIX_API_KEY')}";
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os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "https://app.phoenix.arize.com";
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# configure the Phoenix tracer
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tracer_provider = register(
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project_name="agno-stock-price-agent", # Default is 'default'
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auto_instrument=True # Automatically use the installed OpenInference instrumentation
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)
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agent = Agent(
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name="Stock Price Agent",
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model=OpenAIChat(id="gpt-4o-mini"),
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tools=[YFinanceTools()],
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instructions="You are a stock price agent. Answer questions in the style of a stock analyst.",
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debug_mode=True,
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)
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agent.print_response("What is the current price of Tesla?")

‎cookbook/observability/arize_phoenix_via_openinference_local.py‎

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"""
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This example shows how to instrument your agno agent with OpenInference and send traces to Arize Phoenix.
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1. Install dependencies: pip install arize-phoenix openai openinference-instrumentation-agno opentelemetry-sdk opentelemetry-exporter-otlp
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2. Run `phoenix serve` to start the local collector.
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"""
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import os
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from agno.agent import Agent
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from agno.models.openai import OpenAIChat
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from agno.tools.yfinance import YFinanceTools
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from phoenix.otel import register
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os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "http://localhost:6006";
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# configure the Phoenix tracer
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tracer_provider = register(
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project_name="agno-stock-price-agent", # Default is 'default'
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auto_instrument=True # Automatically use the installed OpenInference instrumentation
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)
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agent = Agent(
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name="Stock Price Agent",
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model=OpenAIChat(id="gpt-4o-mini"),
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tools=[YFinanceTools()],
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instructions="You are a stock price agent. Answer questions in the style of a stock analyst.",
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debug_mode=True,
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)
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agent.print_response("What is the current price of Tesla?")

‎cookbook/observability/langfuse_via_openinference.py‎

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"""
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This example shows how to instrument your agno agent with OpenInference and send traces to Arize Phoenix.
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1. Install dependencies: pip install openai langfuse opentelemetry-sdk opentelemetry-exporter-otlp openinference-instrumentation-agno
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2. Set your Langfuse API key as an environment variables:
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- export LANGFUSE_PUBLIC_KEY=<your-key>
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- export LANGFUSE_SECRET_KEY=<your-key>
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"""
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import base64
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import os
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from agno.agent import Agent
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from agno.models.openai import OpenAIChat
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from agno.tools.yfinance import YFinanceTools
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from openinference.instrumentation.agno import AgnoInstrumentor
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from opentelemetry import trace as trace_api
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from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
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from opentelemetry.sdk.trace import TracerProvider
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from opentelemetry.sdk.trace.export import SimpleSpanProcessor
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LANGFUSE_AUTH = base64.b64encode(
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f"{os.getenv('LANGFUSE_PUBLIC_KEY')}:{os.getenv('LANGFUSE_SECRET_KEY')}".encode()
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).decode()
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os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"] = (
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"https://us.cloud.langfuse.com/api/public/otel" # 🇺🇸 US data region
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)
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# os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"]="https://cloud.langfuse.com/api/public/otel" # 🇪🇺 EU data region
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# os.environ["OTEL_EXPORTER_OTLP_ENDPOINT"]="http://localhost:3000/api/public/otel" # 🏠 Local deployment (>= v3.22.0)
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os.environ["OTEL_EXPORTER_OTLP_HEADERS"] = f"Authorization=Basic {LANGFUSE_AUTH}"
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tracer_provider = TracerProvider()
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tracer_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter()))
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trace_api.set_tracer_provider(tracer_provider=tracer_provider)
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# Start instrumenting agno
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AgnoInstrumentor().instrument()
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agent = Agent(
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name="Stock Price Agent",
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model=OpenAIChat(id="gpt-4o-mini"),
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tools=[YFinanceTools()],
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instructions="You are a stock price agent. Answer questions in the style of a stock analyst.",
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debug_mode=True,
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)
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agent.print_response("What is the current price of Tesla?")

‎cookbook/observability/langfuse_via_openlit.py‎

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import base64
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import os
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from agno.agent import Agent
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from agno.models.openai import OpenAIChat
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from agno.tools.duckduckgo import DuckDuckGoTools
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LANGFUSE_AUTH = base64.b64encode(
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f"{os.getenv('LANGFUSE_PUBLIC_KEY')}:{os.getenv('LANGFUSE_SECRET_KEY')}".encode()
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).decode()
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tracer = trace.get_tracer(__name__)
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import openlit
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# Initialize OpenLIT instrumentation. The disable_batch flag is set to true to process traces immediately.
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openlit.init(tracer=tracer, disable_batch=True)
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debug_mode=True,
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)
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agent.run("What is currently trending on Twitter?")
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agent.print_response("What is currently trending on Twitter?")

‎cookbook/observability/langsmith_via_openinference.py‎

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"""
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This example shows how to instrument your agno agent with OpenInference and send traces to LangSmith.
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1. Create a LangSmith account and get your API key: https://smith.langchain.com/
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2. Set your LangSmith API key as an environment variable:
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- export LANGSMITH_API_KEY=<your-key>
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- export LANGSMITH_TRACING=true
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- export LANGSMITH_ENDPOINT=https://eu.api.smith.langchain.com or https://api.smith.langchain.com
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- export LANGSMITH_PROJECT=<your-project-name>
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3. Install dependencies: pip install openai openinference-instrumentation-agno opentelemetry-sdk opentelemetry-exporter-otlp
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"""
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import os
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from agno.agent import Agent
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from agno.models.openai import OpenAIChat
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from agno.tools.duckduckgo import DuckDuckGoTools
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from opentelemetry.sdk.trace import TracerProvider
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from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
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from opentelemetry.sdk.trace.export import SimpleSpanProcessor
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from opentelemetry import trace as trace_api
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from openinference.instrumentation.agno import AgnoInstrumentor
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endpoint = "https://eu.api.smith.langchain.com/otel/v1/traces"
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headers = {"x-api-key": os.getenv('LANGSMITH_API_KEY'), "Langsmith-Project": os.getenv('LANGSMITH_PROJECT')}
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tracer_provider = TracerProvider()
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tracer_provider.add_span_processor(SimpleSpanProcessor(OTLPSpanExporter(endpoint=endpoint, headers=headers)))
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trace_api.set_tracer_provider(tracer_provider=tracer_provider)
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# Start instrumenting agno
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AgnoInstrumentor().instrument()
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agent = Agent(
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name="Stock Market Agent",
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model=OpenAIChat(id="gpt-4o-mini"),
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tools=[DuckDuckGoTools()],
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markdown=True,
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debug_mode=True,
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)
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agent.print_response("What is news on the stock market?")

‎libs/agno/pyproject.toml‎

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opentelemetry = ["opentelemetry-sdk", "opentelemetry-exporter-otlp"]
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weave = ["weave"]
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openlit = ["openlit", "agno[opentelemetry]"]
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arize = ["arize-phoenix", "agno[opentelemetry]", "opentelemetry-exporter-otlp-proto-grpc", "opentelemetry-distro"]
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langfuse = ["langfuse"]
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# Dependencies for Models
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azure = ["azure-ai-inference", "aiohttp"]
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anthropic = ["anthropic"]

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